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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Generative adaptive resilience: LLM-MILP modeling and strategy generation for port vessel scheduling</title>
      <link>https://trid.trb.org/View/2696228</link>
      <description><![CDATA[Port scheduling systems face critical challenges from escalating disruptions and the limitations of traditional optimization, including rigid feasibility checks, opaque decision-making, and the absence of learning mechanisms. This paper introduces the generative adaptive resilience (GAR) framework, an integrated approach that advances port scheduling from static constraint satisfaction toward a dynamic, learning-enabled process. The GAR integrates generative AI (GAI), mathematical optimization, and evolutionary learning into a closed loop, enabling ports to respond to disruptions and proactively prepare for them. The framework bridges large language models (LLMs) with operations research through the Feasibility-Aware Constrained Decoding (FACD), converting human-interpretable actions (e.g., redirect, delay, resequencing) into mathematically feasible scheduling adjustments with a feasibility guarantee. The GAR explicitly models and exploits synergistic action pairs (e.g., jointly delaying a feeder vessel while redirecting a deep-sea liner), thereby achieving cost reductions through coordination coefficients. Empirical validation using Ningbo Port data shows that the GAR achieves faster policy convergence, reduced high-priority vessel delays, and faster solution time than full-resilience baselines, while maintaining equivalent redundancy. The framework maintains safe collaborative action rates and reduces peak terminal utilization. These results demonstrate the GAR’s concrete advantages: accelerated adaptation, quantifiable service improvements, and computational efficiency, providing ports with a measurable pathway from reactive operations to adaptive, learning-enabled resilience, contributing to risk and safety science in the AI Era.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696228</guid>
    </item>
    <item>
      <title>If You Build It, They May Not Come: Willingness to Participate in Managed EV Charging</title>
      <link>https://trid.trb.org/View/2694400</link>
      <description><![CDATA[Despite the importance of program participation for policy, treatment effects are often measured on self-selected samples. We study electric vehicle (EV) managed charging, intended to reduce electric grid strain by optimally allocating charging across EVs. Prior work finds large impacts of managed charging among households who volunteer for an RCT. In contrast, we test managed charging with an experiment including all EVs within a California utility. Enrollment is low even with high incentives, and we can reject even modest intent-to-treat effects on electricity consumption. Managed charging is less effective than previously thought, underscoring the value of population-wide experiments.]]></description>
      <pubDate>Thu, 30 Apr 2026 09:06:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694400</guid>
    </item>
    <item>
      <title>Spot-fare inspection in urban bus transportation systems: strategy and unpredictability under a Stackelberg game approach</title>
      <link>https://trid.trb.org/View/2692326</link>
      <description><![CDATA[This study addresses the operational implementation of a spot-fare inspection strategy on a proof-of-payment urban bus transportation system, where opportunistic passengers can evade fare payment by the most convenient path. The spot-fare inspection strategy defines the frequency at which the transit authority should control sites of the transportation network to inhibit the action of opportunistic passengers. The operational implementation is done using an unpredictable allocation schedule, where the transit authority selects an allocation schedule of n sites to be controlled (one for each inspection team) each day with some probability. The challenge is to determine the set of allocation schedules and their respective probabilities of being selected whose systematic day-to-day application matches the inspection frequencies defined by the spot strategy. The interaction between transit authority and opportunistic passengers is modeled as a Leader–Follower Stackelberg game, where the decision of opportunistic passengers to evade the fare payment and the path to take depends on the passengers’ observations on the inspection frequencies set by the transit authority. We consider that the transit authority implements a vehicle selective inspection policy and an on-board passenger mass inspection policy, with and without interruption of the bus schedule, representing two real approaches to fare inspection.]]></description>
      <pubDate>Wed, 29 Apr 2026 17:04:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692326</guid>
    </item>
    <item>
      <title>Optimization of Seaplane Transport System in Greece with the Use of Conjoint Analysis</title>
      <link>https://trid.trb.org/View/2579517</link>
      <description><![CDATA[Research was conducted on optimizing the seaplane transportation system in Greece using the Choice-Base Conjoint Analysis method. Due to its geographical morphology and its level of touristic development, Greece is considered an ideal country for using seaplanes. A sample of 216 people were asked questions related to the utility of seaplanes, the quality of seaplane services, and seaplanes’ quality characteristics as set mainly by the Greek business world but also by their standards of use from foreign tourist countries in the field of touristic services. The results were the basis for designing scenarios simulated in market conditions. These scenarios were evaluated in comparison to each other regarding their quality characteristics and the scale of their appeal to the public regarding their contribution to a more effective use of seaplanes. It was found that the optimal seaplane transport system is as follows: The seaplane capacity consists of 18 persons, the frequency of the flights is three (3) times per day, no intermediate stop is interposed, the departure times are in the afternoon, and the cost of the ticket is equal to the cost of traveling by car. The attributes were found to have the following importance: (i) ticket cost, 45.13%; (ii) density of the flight, 21.76%; (iii) intermediate stops, 15.61%; (iv) capacity and size of the seaplane, 11.14%; and (v) departure times, 6.35%. This optimal system gathered 77.28% of respondents who prefer it over the base system, with a rate of 22.72%.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579517</guid>
    </item>
    <item>
      <title>Flexible maintenance in aircraft rotation models: a modular component of the digital airline twin</title>
      <link>https://trid.trb.org/View/2682774</link>
      <description><![CDATA[The planning processes of an airline are complicated due to the multitude of options. Additionally, it takes a long period of time from the first planning steps, like creating the network and defining the fleet, until the flights are executed. The development of the digital airline twin (DAT) at the German Aerospace Center (DLR) is motivated by the objective of simulating and optimizing these processes. The DAT will be a general tool which can be used for airlines with different business strategies and to answer various research questions. By dividing the processes into smaller sub-processes, each model can calculate larger instances and feedback loops can be included to iterate the results. One of these planning processes is the generation of aircraft rotations, which are sequences of specific flights within a given time frame that are later assigned to individual aircraft, known as aircraft rotation problem. This includes the necessity for sufficient availability for regular maintenance for each rotation in order to ensure operational efficiency and safety. To enable flexible, feedback-driven use within the DAT, different simplifications are implemented that leave space for further, more detailed airline planning steps. This paper presents two approaches. Both including sufficient availability for maintenance in each aircraft rotation for an airline to later schedule specific maintenance events. Moreover, the results regarding a one-week flight schedule are compared and the suitability of the approaches for the DAT is discussed.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682774</guid>
    </item>
    <item>
      <title>When to adopt Demand-Responsive Transport systems instead of regular public transport</title>
      <link>https://trid.trb.org/View/2682755</link>
      <description><![CDATA[Demand Responsive Transport (DRT) systems provide versatile transport operations and are capable of quickly adjusting to fluctuating passenger demand. Unlike traditional public transport (PT), which operates with fixed routes and schedules, DRTs offer flexibility in vehicle routes, fleet sizes, and schedules. This flexibility is an intrinsic characteristic of DRT systems and a key attribute in their design and associated decision-making processes. However, flexibility also presents significant design challenges, due to the multitude of potential configurations and the unique characteristics of each service area. As practice shows, the effectiveness of DRT configurations is heavily influenced by demand levels. Highly flexible operations are typically suited for low-demand areas, whereas higher demand may require reduced flexibility to maintain system efficiency. Furthermore, demand may grow to a point where the operators may question whether to continue operating as a DRT or shift to traditional regular public transport, with predefined routes and schedules, and more efficient operation. This work studies how demand levels and characteristics can be used in the decision to adopt a DRT system, instead of PT. The problem was addressed through the simulation of various demand scenarios in a virtual environment, thus comparing the performance of the different transport systems. In the scenario analysed, it was possible to identify a demand threshold where the DRT system is more efficient, while higher demand favours the fixed-route system. However, it is important to note that this threshold may be significantly influenced by the specific characteristics of the service area where the system will operate.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682755</guid>
    </item>
    <item>
      <title>Optimal vehicle fleet and charging strategy for electric ground service equipment at airports based on flight schedule</title>
      <link>https://trid.trb.org/View/2687381</link>
      <description><![CDATA[As the aviation industry transitions towards a green and low-carbon future, deploying electric ground service equipment (E-GSE) and integrating photovoltaic (PV) generation at airports can reduce energy consumption and emissions. This study proposes a two-stage optimization framework. In the first stage, a long short-term memory (LSTM) neural network is employed to forecast seasonal PV power generation, aiming to mitigate the impact of PV output volatility on downstream decision-making. In the second stage, a techno-economic optimization model is formulated, incorporating vehicle operation time constraints, time-of-use electricity pricing, and PV generation, to minimize the annual total cost for airport ground operation. The framework simultaneously optimizes vehicle procurement, charger allocation, and charging schedules. To enhance computational efficiency, the model is linearized, and a Benders decomposition-based mixed-integer programming approach is introduced to solve large-scale instances efficiently. A case study based on real flight schedule data from Guangzhou Baiyun International Airport demonstrates the effectiveness of the proposed method. The proposed framework identifies an optimal airside PV capacity of 559.60 kW, reducing the airport ground operator’s annual total cost by 624,042.60 CNY relative to the no-PV case and achieving a PV payback period of 3.04 years. Compared with conventional vehicle-to-pile ratio planning, the optimized strategy reduces the E-GSE fleet from 198 to 162 and decreases the number of fast chargers from 89 to 73, corresponding to reductions of approximately 18% in both categories.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:58:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2687381</guid>
    </item>
    <item>
      <title>A comparative evaluation of mobile charging pods for electric bus operations</title>
      <link>https://trid.trb.org/View/2679098</link>
      <description><![CDATA[Recent advances in battery technology and the global shift toward sustainable transport have accelerated the adoption of electrified public transit systems. However, the implementation of such systems is often constrained by the need for large battery capacities and the high costs associated with stationary charging infrastructure. This study investigates the potential of Mobile Autonomous Charging Pods (MAPs) which are autonomous mobile charging vehicles as an innovative and cost-effective strategy to support the electrification of high-frequency urban bus lines. Using microscopic simulation for inner-city trunk lines in Stockholm, three charging configurations are evaluated: (i) depot-only charging, (ii) depot charging combined with end-station charging, and (iii) depot charging supported by MAPs. Results show that the MAP-based approach enables a reduction in total battery capacity by up to 67% compared to the depot-only strategy and yields total cost savings of over 7 million USD in total cost of ownership across an 11-year horizon. In addition to reducing capital and grid connection costs, MAPs offer greater operational flexibility and resilience by decentralizing energy delivery and enabling dynamic in-motion or stationary charging. The findings highlight MAPs as a scalable and economically viable solution that complements traditional depot infrastructure, offering a path toward more adaptable and efficient electric public transport networks.]]></description>
      <pubDate>Fri, 27 Mar 2026 10:13:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679098</guid>
    </item>
    <item>
      <title>Utilizing and optimizing non-disrupted lines for evacuating passengers in Urban rail transit networks during disruptions</title>
      <link>https://trid.trb.org/View/2643219</link>
      <description><![CDATA[In urban rail transit operations, conventional disruption management measures, such as train rescheduling and bus bridging services, play a crucial role in alleviating passenger evacuation pressures. Despite their utility, these measures often fall short during peak hours or in densely populated downtown areas due to delayed responses and capacity limitations. Addressing this gap, this study introduces an approach to efficiently manage large-volume evacuations by guiding passengers to alternative paths comprised of the non-disrupted lines within the urban rail network to complete their trips, alongside adjusting train schedules of these non-disrupted lines to enhance capacity for the influx of rerouted passengers. Essentially, this approach utilizes and optimizes non-disrupted lines to evacuate passengers. To tackle this issue, this study develops four mathematical optimization models aimed at optimizing passenger re-routing and adjusting train schedules. These models cater to different scenarios: whether passengers independently choose their paths or adhere to path guidance, and whether train schedules are adjusted. The inclusion of a Path-Sized Logit model within the optimization framework accurately reflects passenger path-choice behaviours, while an iterative algorithm is introduced to tackle the nonlinear models. Applied to a case study of the Zhengzhou Metro, the implementation of the disruption management schemes obtained from these models and algorithm significantly increases the number of affected passengers completing their trips and minimizes passenger delays during disruptions, thereby enhancing the urban rail transit network's resilience. Moreover, the findings from this study offer valuable insights into line redundancy analysis and enable a targeted measure to manage diverse passenger needs during disruptions. These insights provide a foundation for urban rail transit operators to manage disruptions more reliably and efficiently, ensuring a higher level of preparedness for future disruptions.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643219</guid>
    </item>
    <item>
      <title>Real-Time Intelligent Landing-Management Under Urban Unpredictable Operations</title>
      <link>https://trid.trb.org/View/2561875</link>
      <description><![CDATA[The integration of electric Vertical Takeoff and Landing (eVTOL) vehicles into urban transportation presents challenges in scalability, real-time adaptability, and operational efficiency. This study introduces an Agent-Based Modeling (ABM) framework for traffic management, dynamically assigning eVTOLs to landing pads based on real-time data to enhance efficiency in congested urban environments. A comparative analysis is conducted across various scheduling and sequencing approaches, including Mixed-Integer Linear Programming (MILP), Time-Advance (TA), heuristic methods, receding horizon scheduling, reinforcement learning (RL)-based frameworks, and decentralized agent-based strategies. While MILP and TA offer structured scheduling, they struggle with scalability. Heuristic and receding horizon methods improve adaptability but require frequent recomputation, and RL-based approaches show promise but demand extensive training. Current Decentralized models support distributed decision-making but face efficiency constraints at scale. The proposed ABM framework effectively manages 200 eVTOLs with near-linear computational scaling, facilitating real-time negotiations and reducing computational bottlenecks seen in centralized models. Simulation results indicate improved assignment efficiency, landing pad utilization, and reduced negotiation times under high-density conditions. As UAM systems expand, ABM may contribute to operational resilience. Future work will explore integrating environmental factors to further enhance robustness.]]></description>
      <pubDate>Mon, 23 Mar 2026 17:14:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561875</guid>
    </item>
    <item>
      <title>Unified Scheduling Model for High-Speed Train Timetable Optimization and Rescheduling Based on Deep Reinforcement Learning</title>
      <link>https://trid.trb.org/View/2561870</link>
      <description><![CDATA[Train schedule consists of two major phases, train timetable optimization (TTO) and train timetable rescheduling (TTR), which are interconnected with each other and aim to maintain the safety and punctuality of high-speed railway operations under ideal conditions and unexpected disturbances. However, current preparation and adjustment of train timetables face challenges in real-time responsiveness and poor performance on large-scale instances. To alleviate these problems, we propose a unified scheduling model based on deep reinforcement learning (DRL) for both TTO and TTR problems with similar formulations. The key components of our approach include a state representation utilizing the Markov decision process that captures global train and station characteristics, and a policy network that extracts information from this representation to sequentially construct the train departure order. The main benefits of our framework include adaptability to different stopping plans and delay scenarios, decoupling from the problem size, and ensuring the feasibility of generated schemes. Furthermore, to improve the solution quality, we integrate the learned decision policies with a local search method, enabling the scalability of the model with little additional computation cost. Experiments on extensive TTO and TTR instances of the Beijing-Shanghai high-speed railway line demonstrate the effectiveness and practicality of our approach. Our DRL-based method outperforms all the heuristic rules and commercial solvers without retraining the model on various problem sizes, especially on large-scale cases under limited calculation time.]]></description>
      <pubDate>Mon, 23 Mar 2026 17:14:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561870</guid>
    </item>
    <item>
      <title>Train dispatching strategy for high-speed railway networks considering special trains</title>
      <link>https://trid.trb.org/View/2669894</link>
      <description><![CDATA[In real train operations, the train order and handover time of special trains can impact interconnected routes and dispatching efficiency. Therefore, maintaining the planned order or handover time of special trains is a practical and significant consideration for train dispatchers in China. This paper examines three different dispatching strategies for special trains (i.e. cross-bureau trains, cross-line trains). With the objective of minimizing total train delay time, a train timetable rescheduling model is developed, incorporating partially restricted train order and handover times for special trains. Experiments with the CPLEX solver evaluate strategies performance and applicability using real-world data from the Beijing-Shanghai high-speed railway, considering solution quality, operation stability and computational efficiency under initial delay disturbance. Results show that the proposed dispatching strategy, which is entirely fixed based on real-world conditions, can achieve a feasible solution within a reasonable time limit, albeit with some costs for other trains.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669894</guid>
    </item>
    <item>
      <title>Formulating the Electric Bus Fleet Scheduling Problem for Reinforcement Learning</title>
      <link>https://trid.trb.org/View/2665874</link>
      <description><![CDATA[The transition to electric public transportation introduces new challenges in scheduling and operations due to battery constraints and charging requirements. To address these challenges, we propose a simulator designed for reinforcement learning (RL) based approaches to the electric bus fleet scheduling problem. Our work focuses on defining the state space, action representation, and overall simulator functionality to enable effective training and evaluation of RL agents. To validate our solution, we conduct initial experiments using a PPO (Proximal Policy Optimization) agent with a transformer-based architecture and implement automated testing to verify the correctness of generated schedules. Our results confirm that the simulator produces feasible solutions, providing a basis for future research in applying RL to electric bus scheduling.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665874</guid>
    </item>
    <item>
      <title>Integrated demand-side management and timetabling for an urban rail transit line: A Benders decomposition approach</title>
      <link>https://trid.trb.org/View/2625900</link>
      <description><![CDATA[The intelligent upgrading of metropolitan rail transit systems has made it feasible to implement demand-side management policies that integrate multiple operational strategies in practical operations. However, the tight interdependence between supply and demand necessitates a coordinated approach combining demand-side management policies and supply-side resource allocations to enhance the urban rail transit ecosystem. In this study, we propose a mathematical and computational framework that optimizes train timetables, passenger flow control strategies, and trip-shifting plans through the pricing policy. Our framework incorporates an emerging trip-booking approach that transforms waiting at the stations into waiting at home, thereby mitigating station overcrowding. Additionally, it ensures service fairness by maintaining an equitable likelihood of delays across different stations. We formulate the problem as an integer linear programming model, aiming to minimize passengers’ waiting time and government subsidies required to offset revenue losses from fare discounts used to encourage trip shifting. To improve the computational efficiency, we develop a Benders decomposition-based algorithm within the branch-and-cut method, which decomposes the model into train timetabling with partial passenger assignment and passenger flow control subproblems. We propose valid inequalities based on our model’s properties to strengthen the linear relaxation bounds at each node of the branch-and-bound tree. Computational results from proof-of-concept and real-world case studies on the Beijing metro show that our solution method outperforms commercial solvers in terms of computational efficiency. We can obtain high-quality solutions, including optimal ones, at the root node with reduced branching requirements thanks to our novel decomposition framework and valid inequalities. Our integrated optimization approach reduces the fleet size for operators by at least 8.33 % and decreases the waiting time of passengers on the tested instances, thereby validating the effectiveness of our proposed methods.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625900</guid>
    </item>
    <item>
      <title>Integrated air-rail scheduling: A branch-and-price approach for adaptive passenger-centric planning</title>
      <link>https://trid.trb.org/View/2633501</link>
      <description><![CDATA[This paper addresses the air-rail schedule synchronisation problem by proposing a novel approach that designs integrated flight and train schedules from scratch. A passenger-centric approach is employed, considering a set of travel preference criteria: door-to-door travel time, price, and transportation mode. The problem is formulated as an adapted version of a Multi-Commodity Flow (MCF) problem on a time-expanded network, and solved through a branch-and-price procedure. To speed-up the solution process, we propose to couple the resolution of the column-generation sub-problem with a pattern search, performed in a preprocessing phase. The proposed methodology is tested on the French transportation network over a five-month period, considering 1500 commodities. The complete schedule is generated in twelve hours, including the preprocessing time. The final schedule satisfies over 95 % of passenger travel preferences, demonstrating the effectiveness of the approach in optimising multimodal connectivity.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633501</guid>
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